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Symbolic Artificial General Intelligence(AGI)

Symbolic AGI: A Journey into Understanding Artificial Intelligence


What is Symbolic AGI?


Symbolic Artificial General Intelligence(AGI) is a type of artificial intelligence that relies on symbolic representations of knowledge and reasoning. 

Symbolic representations use symbols and rules to represent knowledge, and reasoning is performed by manipulating these symbols according to the rules.

Symbolic AGI has a long and rich history, dating back to the early days of AI research. It has achieved significant successes in a variety of domains, including chess playing, natural language processing, and robotics.

Symbolic Artificial General Intelligence(AGI)

History of Symbolic Artificial General Intelligence(AGI)

Tracing the history of Symbolic AGI reveals a fascinating journey of evolving ideas and relentless pursuit of artificial intelligence resembling human-like intelligence. Here are some key milestones:

Early Seeds (1950s-1960s):

  • The birth of AI: The formal field of AI emerges in the 1950s, with pioneers like Alan Turing and John McCarthy laying the groundwork for symbolic approaches.
  • Logics and rules: Formal logic systems like propositional logic and first-order logic become the foundation for knowledge representation and reasoning.
  • Expert systems: Early applications emerge in medicine, finance, and other domains, using rule-based systems to mimic the expertise of human specialists.

Golden Age (1970s-1980s):

  • Rise of knowledge representation: Languages like Lisp and Prolog are developed specifically for manipulating symbolic knowledge.
  • Planning and problem-solving: AI systems designed for chess playing and robotic control showcase the strengths of symbolic reasoning for planning and action selection.
  • Knowledge-based systems: Cyc, a massive knowledge base of common-sense reasoning and facts, is initiated, aiming to capture the breadth of human knowledge in symbolic form.

Challenges and Diversification (1990s-2000s):

  • The “AI winter”: Funding and enthusiasm for symbolic AI decline as limitations like brittleness and slow learning become apparent.
  • Rise of machine learning: Neural networks and statistical approaches gain popularity for their data-driven learning capabilities.
  • Hybrid approaches: Researchers begin exploring ways to combine symbolic reasoning with machine learning for greater robustness and adaptability.

Renewed Interest and Exploration (2010s-Present):

  • Symbolic reasoning for deep learning: Projects like neural theorem provers and neuro-symbolic systems aim to integrate symbolic logic into deep learning frameworks.
  • Focus on explainability and transparency: Concerns about “black box” AI models call for symbolic approaches that offer interpretable reasoning processes.
  • Rise of embodied AI: The need for intelligent robots interacting with the real world rekindles interest in symbolic reasoning for embodied cognition and planning.

The journey of Symbolic AGI is far from over. While true human-level intelligence remains elusive, the constant evolution of technology and research keeps the dream alive. The future holds the potential for breakthroughs in hybrid approaches, explainable AI, and embodied intelligence, paving the way for a future where humans and machines collaborate with symbolic and neural capabilities.

The history of Symbolic AGI is a testament to human ingenuity and perseverance. Through continued research, collaboration, and exploration, we can harness the power of symbolic reasoning and other approaches to build a future where AI empowers and benefits humanity.

Symbolic Artificial General Intelligence(AGI)

Key functions of Symbolic Artificial General Intelligence(AGI)

Here are the key functions of Symbolic AGI, illustrated with visual examples:

1. Reasoning and Inference:

  • Draw conclusions from incomplete or uncertain information.
  • Combine multiple pieces of knowledge to reach new understandings.
  • Solve problems logically and systematically.

2. Planning and Problem-Solving:

  • Set goals and develop strategies to achieve them.
  • Break down complex tasks into manageable steps.
  • Anticipate potential obstacles and devise solutions.

3. Learning and Adaptation:

  • Acquire new knowledge and skills from experience or instruction.
  • Update its knowledge base and reasoning rules based on new information.
  • Adjust its behavior to adapt to changing circumstances.

4. Natural Language Understanding and Generation:

  • Comprehend human language in all its nuances and complexities.
  • Engage in meaningful conversations with humans.
  • Generate fluent and coherent text and speech.

5. Knowledge Representation and Reasoning:

  • Store and organize knowledge in a structured and accessible way.
  • Manipulate knowledge using symbolic operations to draw inferences and make decisions.
  • Utilize knowledge to solve problems and generate new ideas.

6. Contextual Understanding and Adaptability:

  • Grasp the context of a situation, including relevant background information and social cues.
  • Apply knowledge and reasoning in a context-sensitive manner.
  • Adapt its behavior to different situations and social norms.

7. Creativity and Innovation:

  • Generate novel ideas and solutions.
  • Imagine new possibilities and explore alternative pathways.
  • Engage in creative activities like art, music, and literature.

8. Metacognition and Self-Awareness:

  • Reflect on its own thought processes and capabilities.
  • Monitor its own performance and identify areas for improvement.
  • Develop a sense of self and its place in the world.
Symbolic Artificial General Intelligence(AGI)

Symbolic Artificial General Intelligence(AGI): Challenge and Impact

Symbolic AGI has also faced challenges. It can be brittle and difficult to scale, and it can be slow to learn from new data.

In recent years, there has been renewed interest in symbolic AGI. This is due to a number of factors, including the following:

  • The limitations of machine learning approaches, such as the “black box” problem and the difficulty of generalizing to new situations.
  • The need for explainability and transparency in AI systems.
  • The rise of embodied AI, which requires symbolic reasoning for planning and decision-making in the real world.

The future of symbolic AGI is uncertain. However, there is potential for this approach to play a significant role in the development of artificial general intelligence (AGI).

Here are some specific areas where symbolic AGI could make a significant impact:

  • Explainable AI: Symbolic approaches offer interpretable reasoning processes, which can be essential for building trust and transparency in AI systems.
  • Embodied AI: Symbolic reasoning is essential for planning and decision-making in the real world, which is a key challenge for embodied AI.
  • Hybrid approaches: Combining symbolic reasoning with machine learning can lead to systems that are more robust, adaptable, and efficient.

By continuing to research and develop symbolic AGI, we can build systems that are more intelligent, capable, and beneficial to humanity.

Here are some of the key concepts and terms associated with symbolic AGI:

  • Logical atoms: The basic building blocks of symbolic representations, like objects, properties, and relations.
  • Production rules: Conditional statements used for reasoning, mapping states to actions or conclusions.
  • Inference engine: Software system that manipulates symbols and rules to draw logical conclusions.
  • Model-based reasoning: Simulating situations and scenarios in the world to guide decision-making.
  • Situation Calculus: A formal language for representing actions, their effects, and the resulting world states.
  • Planning and scheduling: Generating sequences of actions to achieve goals within constraints.
  • Natural language understanding: Interpreting the meaning and intent behind human language.
  • Natural language generation: Producing fluent and context-aware text in response to stimuli.
  • Propositional logic and First-Order Logic: Formal systems for representing and reasoning about logical relationships.
  • Reasoning agents: Autonomous entities that make decisions and act based on their knowledge and goals.
  • Bayesian Networks: Probabilistic models representing relationships between variables and their uncertainties.
  • Common-Sense Reasoning: Applying intuitive knowledge about the world for efficient understanding and decision-making.
  • Non-Monotonic Reasoning: Handling situations where new information may invalidate previous conclusions.
  • Embodied Cognition: The interaction between an AI system’s mental processes and its physical body.
  • Sensorimotor Control: Coordinating sensors and actuators to interact with the physical environment.
  • Multi-Agent Systems: Systems composed of multiple interacting agents, simulating social and collaborative scenarios.
  • Reinforcement Learning: Learning through trial and error, receiving rewards for successful actions.
  • Explainable AI (XAI): Making AI decisions and reasoning processes transparent and understandable to humans.
  • Moral and Ethical Considerations: Addressing the ethical implications of developing and deploying AGI systems.
  • Human-AI Interaction (HAI): Designing how humans and AI systems can interact effectively and safely.
Symbolic Artificial General Intelligence(AGI)

Type of Symbolic Artificial General Intelligence(AGI)

While Symbolic AGI remains a theoretical future for artificial intelligence, within it exists a fascinating diversity of potential approaches. 

Here are some key types of Symbolic AGI:

1. Logic-based AGI: This approach is centered around formal logic systems like propositional logic and first-order logic. Knowledge is represented using logical formulas, and reasoning happens through manipulating these formulas according to established rules of inference. Examples include theorem provers and expert systems relying on logic rules.

2. Model-based AGI: This type focuses on building internal models of the world, including objects, their properties, and relationships between them. Reasoning involves manipulating and simulating these models to predict possible outcomes or make decisions. This aligns with approaches like situation calculus and belief networks.

3. Language-based AGI: This emphasizes natural language as the primary tool for knowledge representation and reasoning. Sentences and their interrelationships form the knowledge base, and reasoning uses natural language inferences and semantic rules to navigate and understand the world. This draws inspiration from projects like Cyc and WordNet.

4. Hybrid AGI: Recognizing the strengths and limitations of each approach, hybrid AGI seeks to combine them. For example, logic might be used for high-level reasoning, while neural networks handle sensory perception and low-level learning. This approach is still in its early stages but holds great promise for achieving true AGI.

5. Embodied AGI: Beyond pure reasoning, this type emphasizes the importance of embodiment for realizing AGI. An embodied AGI would interact with the world through a physical body, using its senses and motor skills to gather information and act on its conclusions. This adds a crucial layer of grounding and interaction to the reasoning process.

Symbolic Artificial General Intelligence(AGI)

Specific Research into Symbolic Artificial General Ìntelligence(AGI)

While research into Symbolic AGI is widespread, finding projects explicitly labelled as “Symbolic AGI” is rare. This is because the field is still undergoing rapid development and terminology hasn’t fully solidified. 

However, several ongoing projects embody the principles of Symbolic AGI and its various approaches:

1. DeepMind and Neural Theorem Provers: DeepMind, known for its work in Go and StarCraft AI, is exploring the integration of neural networks and symbolic reasoning, particularly through neural theorem provers. These projects aim to train neural networks to manipulate logical formulas effectively, potentially accelerating mathematical and scientific discovery.

2. Project Cogito: This initiative by IBM Research focuses on building a cognitive architecture inspired by human brain structures. It uses a knowledge base represented in multiple formats, including symbols, and employs reasoning mechanisms informed by logic and cognitive psychology.

3. Cyc and OpenCyc: Cyc is a massive knowledge base developed by Doug Lenat, encoding common-sense knowledge and reasoning rules using symbols and logic. OpenCyc is a publicly available version of this project, encouraging researchers to add knowledge and explore its potential for various AI applications.

4. Soar: This cognitive architecture developed by John Laird combines symbolic reasoning with production rules and decision-making capabilities. Soar has been applied to various domains, including robot control, game playing, and medical diagnosis, demonstrating its versatility in symbolic AI tasks.

5. COMET: This project from SRI International focuses on building a common-sense reasoning system based on logical representations and probabilistic inference. COMET aims to develop robust reasoning capabilities for robots and other AI systems operating in complex, dynamic environments.

Symbolic Artificial General Intelligence(AGI)

Symbolic Artificial General Intelligence(AGI) Projects: A Glimpse into the Future of AI

While achieving true Symbolic AGI remains a fascinating yet distant goal, several exciting projects are actively exploring its potential and laying the groundwork for future breakthroughs. 

Here are a few noteworthy examples:

1. DeepMind and Neural Theorem Provers: Imagine AI that seamlessly combines the pattern recognition of neural networks with the logic and deduction of symbolic reasoning. DeepMind’s research in neural theorem provers aims to do just that. By training neural networks on vast datasets of mathematical proofs, they hope to accelerate theorem proving and unlock new discoveries in science and mathematics.

2. Project Cogito from IBM Research: Inspired by the human brain’s structure and function, Project Cogito builds a cognitive architecture using a multi-format knowledge base and diverse reasoning mechanisms. This allows for flexible handling of information, from symbols and logic rules to visual and sensor data, offering a promising pathway towards robust AI capable of interacting with the real world.

3. Cyc and OpenCyc: This vast knowledge base, developed by Doug Lenat, encodes common-sense knowledge and reasoning rules using symbols and logic. OpenCyc, its publicly available version, empowers researchers to contribute their own knowledge and explore its potential for various applications, from education and robotics to natural language processing.

4. Soar: A Cognitive Architecture with Teeth: This versatile system combines symbolic reasoning with production rules and decision-making capabilities. Soar has proven its mettle in diverse domains, from robot control and game playing to medical diagnosis, showcasing its potential for adaptable and intelligent AI systems.

5. COMET: Navigating the Uncertain Sea of Common Sense: This project from SRI International tackles the challenge of common-sense reasoning, crucial for real-world intelligence. COMET uses logic representations and probabilistic inference to build robust reasoning systems for robots and AI navigating dynamic and unpredictable environments.

Symbolic Artificial General Intelligence(AGI)

Institution focused on developing “The Symbolic Artificial General Intelligence(AGI)” 

There isn’t one single institution solely focused on developing “The Symbolic AGI.” Symbolic AGI is still a theoretical future for artificial intelligence, and research in this area is spread across diverse teams and institutions worldwide.

However, several research groups and institutions are actively contributing to research and development related to the different types and approaches of Symbolic AGI. 

Here are some notable examples:

1. DeepMind: As mentioned earlier, DeepMind is exploring the integration of neural networks and symbolic reasoning, particularly through neural theorem provers. They have achieved significant progress in areas like logical reasoning and mathematical problem-solving.

2. OpenAI: This research laboratory founded by Elon Musk and others is conducting research on various aspects of AI, including natural language processing, reinforcement learning, and robotics. While not explicitly focused on Symbolic AGI, their work on symbolic reasoning and knowledge representation contributes to the broader field.

3. Stanford University: The Stanford Artificial Intelligence Laboratory (SAIL) is home to numerous research groups working on different aspects of AI, including natural language processing, robotics, and machine learning. Some projects within SAIL, like COMET and Soar, directly contribute to research on symbolic reasoning and cognitive architectures.

4. Carnegie Mellon University: The Robotics Institute at Carnegie Mellon has a long history of research in AI and robotics, with projects exploring symbolic reasoning and knowledge representation for robot planning and decision-making.

5. International Joint Conference on Artificial Intelligence (IJCAI): While not an institution itself, IJCAI is a major conference and forum for AI research. It features diverse research on symbolic reasoning, knowledge representation, and other aspects relevant to Symbolic AGI, showcasing the breadth of ongoing work in this field.

These are just a few examples, and many other universities, research labs, and private companies are actively contributing to the field of Symbolic AGI. It’s important to note that research in this area is collaborative and open-source, with frequent exchange of ideas and knowledge between different institutions and researchers.

Therefore, instead of pinpointing a single institution solely responsible for developing The Symbolic AGI, it’s more accurate to see it as a collaborative effort across various research communities worldwide.

Symbolic Artificial General Intelligence(AGI)

Symbolic Artificial General Intelligence(AGI) Technology

Achieving Symbolic AGI is a complex puzzle with many pieces, and the technologies involved are diverse and constantly evolving. 

Here are some key technological pillars fueling the quest for human-like machine intelligence:

1. Knowledge Representation and Reasoning:

  • Symbolic languages: These languages, like first-order logic, encode knowledge using symbols and relationships, enabling formal logical manipulations for reasoning and inference.
  • Knowledge graphs: These interconnected web-like structures capture relationships between entities and concepts, offering a rich tapestry of knowledge for AI to navigate.
  • Reasoning engines: These software systems handle logical deductions and inferences, drawing conclusions from the structured knowledge base.

2. Machine Learning and Neural Networks:

  • Deep learning: These powerful algorithms excel at pattern recognition and data extraction, offering a valuable layer of understanding for raw sensory information and large datasets.
  • Neuro-symbolic systems: These hybrid approaches combine the strengths of neural networks and symbolic reasoning, allowing AI to learn from data while utilizing logical structures for efficient knowledge processing.
  • Probabilistic reasoning: Techniques like Bayesian inference offer ways to handle uncertainty and incomplete information, crucial for real-world decision-making.

3. Natural Language Processing:

  • Language understanding and generation: These technologies enable AI to comprehend human language nuances and generate fluent, context-aware communication, fostering natural interaction and knowledge sharing.
  • Dialogue systems: These AI systems engage in meaningful conversations, asking questions, clarifying ambiguities, and providing relevant information, paving the way for human-like interactions.
  • Semantic reasoning: Understanding the meaning behind words and sentences is crucial for AI to grasp the deeper intent and context of natural language communication.

4. Robotics and Embodiment:

  • Physical robots: Providing AI with a physical body opens doors to real-world interaction and experimentation. Sensory inputs and motor control capabilities allow AI to learn and adapt through embodied experiences.
  • Robotics control systems: These systems translate abstract reasoning and decisions into concrete actions for the robot to execute in the physical world.
  • Sensor fusion: Combining data from multiple sensors like cameras, lidar, and touch sensors provides a richer understanding of the surrounding environment for robust decision-making.

5. Hardware and Computing Power:

  • High-performance computing: Complex reasoning and knowledge manipulation require substantial computational resources. Advancements in hardware and software optimization are crucial for handling the demands of AGI.
  • Cloud computing and distributed systems: Sharing processing power across multiple machines allows for tackling larger and more complex tasks, accelerating the development and testing of AGI algorithms.
  • Neuromorphic computing: Inspired by the human brain’s architecture, these specialized hardware systems aim to improve efficiency and performance for artificial intelligence tasks.
Symbolic Artificial General Intelligence(AGI)

20 Terms in Symbolic Artificial General Intelligence(AGI)

  1. Logical Atoms: The basic building blocks of symbolic representations, like objects, properties, and relations.
  2. Production Rules: Conditional statements used for reasoning, mapping states to actions or conclusions.
  3. Inference Engine: Software system that manipulates symbols and rules to draw logical conclusions.
  4. Model-Based Reasoning: Simulating situations and scenarios in the world to guide decision-making.
  5. Situation Calculus: A formal language for representing actions, their effects, and the resulting world states.
  6. Planning and Scheduling: Generating sequences of actions to achieve goals within constraints.
  7. Natural Language Understanding: Interpreting the meaning and intent behind human language.
  8. Natural Language Generation: Producing fluent and context-aware text in response to stimuli.
  9. Propositional Logic and First-Order Logic: Formal systems for representing and reasoning about logical relationships.
  10. Reasoning Agents: Autonomous entities that make decisions and act based on their knowledge and goals.
  11. Bayesian Networks: Probabilistic models representing relationships between variables and their uncertainties.
  12. Common-Sense Reasoning: Applying intuitive knowledge about the world for efficient understanding and decision-making.
  13. Non-Monotonic Reasoning: Handling situations where new information may invalidate previous conclusions.
  14. Embodied Cognition: The interaction between an AI system’s mental processes and its physical body.
  15. Sensorimotor Control: Coordinating sensors and actuators to interact with the physical environment.
  16. Multi-Agent Systems: Systems composed of multiple interacting agents, simulating social and collaborative scenarios.
  17. Reinforcement Learning: Learning through trial and error, receiving rewards for successful actions.
  18. Explainable AI (XAI): Making AI decisions and reasoning processes transparent and understandable to humans.
  19. Moral and Ethical Considerations: Addressing the ethical implications of developing and deploying AGI systems.
  20. Human-AI Interaction (HAI): Designing how humans and AI systems can interact effectively and safely.
Symbolic Artificial General Intelligence(AGI)

The future of Symbolic Artificial General Intelligence(AGI)

The future of Symbolic AGI is shrouded in both excitement and uncertainty. While achieving true human-level intelligence remains a distant dream, the progress in recent years paints a promising picture for the years ahead. Here are some potential scenarios:

Optimistic Visions:

  • Breakthrough in Reasoning and Planning: New theoretical frameworks or computational architectures could unlock significant leaps in logical reasoning and planning capabilities, opening doors for AGI to tackle complex real-world problems.
  • Hybrid Approaches and Integration: Combining the strengths of symbolic reasoning with deep learning and other techniques could lead to robust and efficient AGI systems capable of both understanding and learning from the world.
  • Emergence of Artificial Creativity: AGI could surpass human limitations in certain domains, leading to advancements in scientific discovery, artistic expression, and technological innovation.
  • Enhanced Human-AI Collaboration: Seamless interaction and knowledge exchange between humans and AGI could revolutionize fields like healthcare, education, and governance.

Cautious Considerations:

  • Limited Understanding of Consciousness: Replicating the true essence of human consciousness may still be beyond our grasp, leading to AGI systems lacking genuine understanding and empathy.
  • Ethical and societal challenges: The vast capabilities of AGI necessitate careful consideration of ethical implications, bias in algorithms, and potential societal disruptions.
  • Control and Safety Concerns: Ensuring the safe and responsible development and deployment of AGI will be paramount, requiring robust security measures and regulations.

The Path Forward:

  • Continuous Research and Development: Continued investment in research, collaboration between diverse disciplines, and open exploration of new ideas are crucial for advancing the field.
  • Focus on Explainability and Transparency: Making AI decisions and reasoning processes transparent is essential for building trust and mitigating potential risks.
  • Public Discussion and Policy Development: Open dialogue about the potential impact of AGI on society and proactive policy development are vital for responsible implementation.

Ultimately, the future of Symbolic AGI depends on the choices we make today. By prioritizing safety, transparency, and responsible development, we can harness the potential of AGI to usher in a future of prosperity and collaboration for humanity.

Symbolic Artificial General Intelligence(AGI)

Conclusion for Symbolic Artificial General Intelligence(AGI)

Symbolic AGI is a type of artificial intelligence that relies on symbolic representations of knowledge and reasoning. 

Symbolic representations use symbols and rules to represent knowledge, and reasoning is performed by manipulating these symbols according to the rules.

Symbolic AGI has a long and rich history, dating back to the early days of AI research. It has achieved significant successes in a variety of domains, including chess playing, natural language processing, and robotics.

However, symbolic AGI has also faced challenges. It can be brittle and difficult to scale, and it can be slow to learn from new data.

In recent years, there has been renewed interest in symbolic AGI. This is due to a number of factors, including the following:

  • The limitations of machine learning approaches, such as the “black box” problem and the difficulty of generalizing to new situations.
  • The need for explainability and transparency in AI systems.
  • The rise of embodied AI, which requires symbolic reasoning for planning and decision-making in the real world.

The future of symbolic AGI is uncertain. However, there is potential for this approach to play a significant role in the development of artificial general intelligence (AGI).

Here are some specific areas where symbolic AGI could make a significant impact:

  • Explainable AI: Symbolic approaches offer interpretable reasoning processes, which can be essential for building trust and transparency in AI systems.
  • Embodied AI: Symbolic reasoning is essential for planning and decision-making in the real world, which is a key challenge for embodied AI.
  • Hybrid approaches: Combining symbolic reasoning with machine learning can lead to systems that are more robust, adaptable, and efficient.

By continuing to research and develop symbolic AGI, we can build systems that are more intelligent, capable, and beneficial to humanity.

https://www.exaputra.com/2024/01/symbolic-artificial-general.html

Renewable Energy

GE Vernova Backs LM Wind Power, KKR Buys EDF Assets

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Weather Guard Lightning Tech

GE Vernova Backs LM Wind Power, KKR Buys EDF Assets

GE Vernova pumps $1 billion into LM Wind Power, and KKR buys EDF’s US and Canada renewables arm. Plus CIP sweeps South Korea’s offshore auction and the CME plans wind derivatives across three continents.

Sign up now for Uptime Tech News, our weekly newsletter on all things wind technology. This episode is sponsored by Weather Guard Lightning Tech. Learn more about Weather Guard’s StrikeTape Wind Turbine LPS retrofit. Follow the show on YouTubeLinkedin and visit Weather Guard on the web. And subscribe to Rosemary’s “Engineering with Rosie” YouTube channel here. Have a question we can answer on the show? Email us!

The Uptime Wind Energy podcast, brought to you by StrikeTape. Protecting thousands of wind turbines from lightning damage worldwide. Visit striketape.com. And now, your hosts.

Allen Hall: Welcome to the Uptime Wind Energy podcast. I’m your host, Allen Hall, and I’m here with Matthew Stead and Yolanda Padron. Rosemary is at GWO training this week. And we have an announcement about Wind Energy O&M Australia 2027. Matthew, you wanna give all the details?

Matthew Stead: Drum roll Um, very pleased to announce that WOMA 2027 will be at the East Pullman Hotel in Melbourne’s east, uh, not the other one, and, uh, 3rd to 5th of March.

Um, the first two days will be two days of wind O&M, uh, conferences, [00:01:00] uh, and then the Friday will be a half-day, uh, training session. More information to come.

Allen Hall: Well, she’s not here, so we can probably just announce it, that Rosemary will be giving a terrific four-hour-long seminar on blades and blade repair, so you sign up now.

Matthew, where do you go if you wanna just check out what’s happening at WOMA

Matthew Stead: 2027? Uh, well, actually, it’s woma2027.com.

Allen Hall: Uh, over at GE Vernova and LM Wind Power, there’s been a whole bunch of turmoil over the last couple of years if you haven’t been paying attention. Well, GE Vernova just injected about a billion dollars into that company.

So although LM recently has shown very little in terms of revenue, it definitely had needed some capital injection in, uh, at least according to the Danish press, the number of employees at the Danish site is about 20 to 30. So it’s really a fraction of what it once was. But [00:02:00] it does seem like GE is paying off all its existing debt and then giving it a little bit of a cash infusion to keep it rolling.

The question really is, is what is GE Vernova gonna do with that business now? Are they planning on keeping it? Are they trying to get s- to get it back to health where they can service the other, uh, OEMs that they manufacture blades for? Or is there a larger action that will happen in the near future?

What do we think?

Matthew Stead: Yeah, I’m really confused by this one. I mean, a cash injection just so that you’re not bankrupt on paper is, um, that’s just playing with money as far as I’m concerned. Or I’m not sure if it’s a US term, but, you know, shuffling deckchairs on the Titanic. It doesn’t– Does it change anything?

Allen Hall: Well, uh, th- they made no announcements about closing facilities. The LM blade facility in North Dakota still appears to be making blades. There’s the TPI factories, which are going through a transition r- right now, appear to be making GE [00:03:00] blades. I, I assume Gaspé up in Canada is still making blades, at least that’s the story.

If GE’s gonna rely upon LM to make blades, they’re gonna need to keep them open. Is, is this more of just keeping the factories open with a skeleton engineering crew and possibly moving the blade design group into the States? Is that– Or India or, or somewhere?

Yolanda Padron: And they’re still selling, right? They’re still selling blades.

It seems like they’re still planning on manufacturing blades. Do we think that maybe- They’re just trying to avoid that whole TPI bankruptcy deal to not have to kind of scrap for parts?

Allen Hall: Yeah, it’s a great question. I think TPI has been producing parts at high quantity, and some of the Things I’ve heard from the industry folk is that TPI is really busy in producing quality blades, and it’s like the bankruptcy transaction is not happening, which is great to hear because the [00:04:00]industry needs blades, and there’s a lot of repowering going on in the United States and a lot of activity in general, so they need blades.

But does LM continue to be a part of that?

Matthew Stead: Yeah, I mean, presumably the TPI, um, whole story only makes LM more important, you know, more important to have, uh, an additional manufacturer and, you know, providing, you know, options for the OEMs.

Allen Hall: It does seem like, though, the GE offshore, GE Vernova offshore is not a thing.

Although I’ve heard a couple of rumors that, yeah, GE Vernova is offering some products for offshore, it doesn’t seem like their heart is in it. I can see that happening. So are they just trying to focus on onshore business, and that’s it for the time being? Just let it play out and, uh, wait until the elections in 2028?

I know that’s gonna get me blocked on YouTube, but that, that does feel like what’s happening at the moment.

Matthew Stead: Yeah, I reckon it looks completely like that.

Yolanda Padron: I mean, it also looks like they’re [00:05:00] just kind of trying to play everything a little bit more safe, right? So they are scaling up, but not as fast as they used to, so scaling the blade sizes.

And then they’re– it seems like they’re, they’re having their FSAs cut quite a bit shorter than they used to, right? So are they maybe just trying to focus on, like, cash up front and just trying to play it safe until they can get their, their footing right again?

Allen Hall: Or is it focus on key customers? I could see GE Vernova actually doing that, that they have a history with certain operators worldwide, and they’re just gonna focus on producing and delivering for those customers.

Because you don’t see a lot of announced orders for GE turbines. Vestas is announcing things practically every week. Nordex is doing something similar. Siemens once in a while. But what you really don’t hear anything from in any quantity at [00:06:00] all at the moment is from GE Vernova. When a company needs cash badly enough, even the crown jewels go on the block.

And EDF, the French state-owned utility, has to fund the upkeep of 57 aging nuclear reactors and build six new ones, so it is selling. EDF has agreed to hand its US and Canada renewables business, EDF Power Solutions, to the private equity firm KKR. The business runs 5.6 gigawatts of renewable assets across the two countries.

Late last year, EDF’s chief executive floated selling anywhere from half to all of the unit in a deal that could be, well, it’s reported to be about $4.2 billion. That’s the latest news I’ve heard. This is a big transaction. KKR is Canadian, right? And is a massive investment firm Uh, which I, I don’t think have a lot of wind at the moment.

Uh, what is the [00:07:00] KKR play here?

Matthew Stead: I, I love this because this is, uh… So obviously I’m Australian, and Macquarie is a big Australian. So, um, Macquarie own a whole lot of wind farm, a whole lot of wind infrastructure. So I just see this as a wonderful g- you know, fight between KKR and Macquarie. And so KKR has a whole lot of, um, they o- they’ve got some, you know, stake in Australian wind farms.

They’ve got some work, you know, through Europe with wind farms. So I, I, I think this is a good thing, just a bit more global competition and a bit more global growth. And I think it’s all coming from the data centers and, you know, the future increase in growth of, um, demand.

Allen Hall: Yolanda, EDF’s wind fleet is a variety of turbines, right?

They have some GE, some Siemens. Anything else in their portfolio?

Yolanda Padron: I think they have a bit of Vestas there too, right? Is it something that we were saying? It’s– I think this is really interesting. Um, I know that there’s not– I mean, of course EDF is the latest, but there’s some [00:08:00] operators that seem to be, um, consolidating into a bit more of those just higher private equity firms, and it’s– Do we think that maybe this is the way that the US is going to lean towards?

I know we talked a lot about leaning towards funding the data centers and maybe a bit more the behind the meter things. Uh, but do we think that maybe that’s the future of the US? There’s a couple of companies that kind of just own all the major infrastructures and then- A

Allen Hall: couple Canadian companies.

Yolanda Padron: And what does it mean for, like, asset management and stuff, like, that’s really, really different from what they’re seeing in their desks in New York and stuff, and just the larger financial models versus what’s happening on the ground, and how will they connect everything?

Allen Hall: It’s a great question.

Matthew Stead: NextEra and Dominion, you know, things are only getting bigger. Scale’s, scale’s coming.

Allen Hall: Yeah. I wonder how much, uh, this transaction will have to go through regulators in the US, uh, because it scares me when you have a, a– such a [00:09:00] large foreign national company. There’s actually two involved in here, right?

So you, you have a, a French company and a Canadian company trying to transact on, in the United States on a lot of assets. Uh, it probably won’t be that quick if there’s any oversight at all. I, I’m guessing that we’ll hear noise about it. So we’re, we’ll have to keep listening to all the news sources about it and, and telling our valued listeners what’s going on.

Because there’s, uh, we know a whole bunch of people that work at EDF and like, love those people and are really concerned about what the future holds for them. I, at least it sounds like upfront that KKR is just gonna continue with operations, but I know, uh, uh, it’s a turbulent time, and if you work there, you, you hopefully things continue the way they’re, they’re supposed to because One of the things about EDF historically has been is that they’re really talented people, that they have hired well over time and that they know what they’re doing.

And every time we, Weather Guard and [00:10:00] Yolanda and I’m sure Matthew have dealt with EDF quite a bit They are on top of what they’re operating. They know how their assets work, and they know how to manage them, and so you’d hate to lose those people in a transaction like this. It would decrease the value of the assets, I would say.

Very interesting transaction.

Matthew Stead: Yeah. But, I mean, what if the counter, what if, um, this is all part of a, a growth strategy? You know, a growth strategy with wind, solar, and battery, you know, providing more power. So it might actually be an opportunity. So, you know, opportunity to do more and some more exciting work across all three disciplines.

Allen Hall: Definitely so. Uh, but it’s a little early. The ink hasn’t dried yet on the contract. So while offshore market pulls back in general, in a lot of places like the United States, another one is racing ahead. In, in South Korea’s latest offshore wind auction, one name walked away with the lion’s share, Copenhagen Infrastructure Partners, CIP.

The Danish fund [00:11:00] secured more than one gigawatt of the 1.8 gigawatts on offer, including the single largest project and the only floating wind winner. And the appetite was record-breaking. They had a whole bunch of developers trying to bid on this. You had about 3.7 gigawatts being bid in, more than twice of the capacity available.

So for a country that only began competitive offshore bidding in 2022, that’s a few short years ago, that market is coming of age. This is a huge announcement by CIP, right? That, uh, they have bid into the system. They’re, they’re winning, and they’re bringing Siemens Gamesa to the table, which we haven’t heard a lot of Siemens Gamesa’s turbines being selected, but this is a massive order and really gonna help secure at least some portion of, of the Siemens Gamesa business.

Matthew, you’re closer to it. In, in South Korea, are you seeing the South Korean industry being built within [00:12:00] the country, or are you seeing, uh, partnerships with surrounding countries like Japan? ‘Cause it doesn’t seem like when– and I’ve looked at some of the South Korea, uh, efforts. It does seem like they’re trying to stand up their own offshore built-in country plan.

Is, is that the goal? You think Siemens is gonna end up building a, a factory in, in South Korea for some of these projects?

Matthew Stead: Maybe a couple of things. First of all, I have to apologize. I think, uh, we were talking the other week, and I, I, I sort of implied that floating offshore wind was dead, and I think we copped a bit of flack from that.

But, uh, anyway, wrong, wrong on, uh,

Allen Hall: floating offshore is dead.

Matthew Stead: Um, but um, you know, I’ve had a fair bit of interaction with, uh, South Korean, um, you know, Philippines, Japan, obviously. I think they’re all trying to get their industries up, but I, I don’t think they’ve got the scale So, you know, I think they, they really need like the Siemens Gamesas, the Vestas’s, um, to come in and, and partner with them.

I just don’t think they’ve got the scale, you know, the, the [00:13:00] installed fleet, the industry to really promote it. And, you know, to get the economies of scale, they’re gonna have to pull in the big existing incumbents. So, you know, good on CIP for, for pulling this off.

Allen Hall: In terms of South Korea industry, I think steel is one of their strongest, uh, industries at the moment, and obviously shipbuilding.

Those are the, that go hand in hand, so to speak. There’s a lot of steel in wind turbines, and particularly in floating offshore wind turbines. It would seem ripe for South Korea to get into that marketplace.

Matthew Stead: I’m not sure the intellectual property is in steel tubes. Um, I, I guess what I’m trying to say is the intellectual property is in the turbine nacelle and the blades and, um, you know, I, you know, correct what I said that, you know, obviously the steel and the steel manufacturing in South Korea is, is pretty amazing.

Um, but yeah, they’re clarifying what I said before.

Allen Hall: So is this gonna turn into the leading floating project in the world? You know, Greenvolt’s gonna happen in the [00:14:00] UK. There’s some talk of things up in Scandinavia. But in terms of speed, will this be one of the leading candidates in t- in getting things in the water just because of the capability of South Korea to, to build at scale?

I

Matthew Stead: think it’s really exciting. Yeah, I, I’m, I’m gonna watch very closely.

Allen Hall: I think this is gonna be amazing. I really do.

Yolanda Padron: I was gonna say, could you imagine, like, a, a turbine and a blade where everything is just perfectly manufactured or close to perfectly manufactured? I g- I went to one farm last week, and there were…

I mean, it was in the States, and there were so many patches on new blades. I was just talking to the people in operations like, “What’s, what’s going on here?” You know? Uh, so it’s just really… I don’t know. This is exciting.

Matthew Stead: Do you think, um, they’ll build a blade factory, Yolanda? Do you think they’ll actually take on the blades?

Yolanda Padron: I don’t know. Uh, I, I mean, it’d, it’d be great for them, I think, right? It’s a new area of business that they’re diving [00:15:00] into.

Allen Hall: If they don’t have to build the building at the port, I think Siemens would be willing to erect something near the shoreline. And in Korea, there’s a lot of major industry right on the shoreline.

It would be relatively easy, I think. You know, ev- it sounds easy now because you’re not actually doing it. But in terms of, you know, building a blade factory on the coastline of United States versus doing it in South Korea, South Korea’s gonna be way easier to do that and at scale quickly. That, that one seems like a win-win.

I d- if there’s any place on the planet that could do it quick besides the UK or, you know, Denmark, someone like Netherlands, someplace like that, Germany, it’s gonna be South Korea.

Matthew Stead: Maybe that’s a bet, you know. So prove me wrong again. My money at the moment is that Nacelles blades won’t be coming from South Korea.

Allen Hall: Well, if they don’t come from South Korea, they’re gonna be on a South Korea-built ship. We’ll be bringing th- those [00:16:00] blades in country. That’s what will happen. So wind is getting its own set of financial instruments, which sounds weird, right? Wind is wind. It’s in a very legacy style industry. The Chicago Mercantile Exchange is planning to launch wind derivatives across three continents, which are contracts that are tied to the grid in Texas, the markets in the UK and Germany, and just the Victoria state in Australia.

So today, most weather hedging happens through one-off over-the-counter deals that are sort of hard to trade and thin on liquidity, so it’s not a commodity you can pass around. A standardized exchange-listed contract changes all that. A utility or a wind farm owner could lock in a hedge in about 15 minutes.

The contracts would settle against independent data that models how much power the wind should have produced in a given place, likely supplied by [00:17:00] the Finnish firm, drum roll, Vaisala. Plans are not final, but they could go live within months. So they’re hedging on the wind. Does this sound like a smart move, or w- what are some of the consequences of this?

Matthew Stead: I think it goes back to that volatility. W- when there’s volatility, people can make money. Um, you know, and a side note, that’s where, that’s where offshore wind comes in because it’s much more predictable. Um, you don’t get the same lulls with offshore wind. Yeah. So I, I, I love all these, these creative ways of, um, generating, generating demand, financial demand.

Allen Hall: It can be played though, right? I mean, that’s one of the things about wind, ’cause each turbine is its own separate little power plant that all connect to a substation, so if you have bought a hedge and the substation goes kaput for 24 hours, you could lose your shirt. It does seem kind of risky, depending on what the scale is here.

If you’re doing all of Texas or all of [00:18:00] Victoria, maybe that makes a little more sense, but yikes. That’s gonna be a rough market.

Yolanda Padron: Yeah, the market’s already open, right? Like, you can bid day ahead, um, instead of just real-time prices. But so this, this would be really interesting for owners, right? To be able to track that a lot better than just that gut feeling, which obviously I know people working in trading aren’t just going off of their gut feeling.

I know it’s a very, very intense thing. Nobody go against me, please. This is very intense, and it’s better– They do a better job than I could ever do. They do great, 10 out of 10. But this– I think this is really interesting for those of us especially who maybe aren’t super in tune with what, uh, all goes into it.

So being able to have something that helps you plan it a bit more for, you know, people like you mentioned earlier, the people that have their home batteries in Australia and are just working on the market itself and maybe [00:19:00] not– don’t have those 10, 20 years of experience of, of actually working on the market.

So this is, this is exciting.

Allen Hall: Does that explain all the weather sources and the weather companies when we go to a wind, a larger wind or solar event that there does seem to be a lot of people offering weather insights? Is that what that’s about, is they can hedge? If you have a slightly better weather model, that would give you an advantage in this kind, kind– really kind of market?

Is that the, the goal of all those weather firms?

Matthew Stead: Uh, absolutely. And, you know, we’re, we’re part of that because, um, ice, ice, um, you know, reduces power output, and ice forecasting and weather forecasting is, uh, really important in, you know, the Nordics, where you don’t want to be promising certain power and find you can’t deliver ’cause everything’s iced up.

So, you know, we, we do work with forecasting companies to improve the, [00:20:00] uh, the quality, and it does have a mer-material difference on, on the financial markets.

Allen Hall: So is that something that we can all get paid for? by these weather companies and these, uh, forecast companies if we provide insights on lightning, so to speak, and icing, uh, is that a revenue chain for at least one of us?

Matthew Stead: Absolutely.

Allen Hall: Maybe I like this more and more. I was, I was very hesitant of this exchange, thinking like, “Oh man, not a, not another highly leveraged situation with energy. That doesn’t sound smart.” But, yeah, if we can make a small fortune, Matthew, I think we should do it.

Matthew Stead: Fun fact, there was a flight from, um, yeah, from London to Australia the other week, um, and it’s a direct flight, you know, so 17 hours, and, uh, there was a change in the weather.

So there was a change in the weather, and that aircraft didn’t have enough fuel to fly to Perth anymore, so it had to land in the outback of Australia.

Allen Hall: No. Did that happen?

Matthew Stead: Yep, because there was a [00:21:00] change in the weather.

Allen Hall: Are there just, like, kangaroos lined up in a runway shape to get the airplane on the ground?

Or how do they– Is there a runway out in the outback that would accommodate a large… That’s a large airplane that’s making a London to Australia trip. Triple 7380? It

Matthew Stead: was a Dreamliner. Um, but, um, it, yeah, it landed in Kalgoorlie. So Kalgoorlie’s a mining town. Yeah, they’ve got, they’ve got big stuff in Kalgoorlie.

Allen Hall: In this quarter’s PES Wind magazine, in which there is a whole bunch of great articles, a interesting article about grease. Grease not the country, although I would love to go visit Greece. Grease the lubricant that’s in all our bearings and keeps the world moving at any one particular time. Uh, Sh-Shell was talking about doing a lot of research on grease, and when poor lubrication, uh, happens, it’s one of the leading causes of bearing failure.

And so when you see a bearing all tore up, usually the first indication is, is there’s something wrong with the grease. Uh, [00:22:00] so Sh-Shell and bearing maker SKF and the University of, uh, Twente joined forces to answer a deceptively simple question: How do you predict when grease inside a bearing will let go?

Well, their answer comes down to film thickness. The microscopic layers of grease that keeps the steel from grinding on each other is the magic variable. The work won a major tribology award and is already feeding into, uh, some of the tools that operators use to schedule relubrication before a bearing fails.

And It all comes down to lubrication. That’s the lifetime of a wind turbine. There’s so many pieces that are rotating and are heavily loaded with really complicated bearing surfaces. If you don’t have the grease right, it’s just not gonna work. And what’s happening at Shell is one of those pieces, and we’re [00:23:00] learning so much more.

And as we, uh, evolve in the technology and become smarter about the molecules we use and how we use them, uh, this is gonna have a big impact. And I know, Yolanda, you’ve been up to– Well, you’ve been to a couple of wind farms recently. Do you s- see– still see huge grease problems that I usually see when I’m on site?

Matthew Stead: Mm-hmm.

Yolanda Padron: I didn’t think that was an issue that was gonna go away anytime soon. But it’s good to know that, that there’s something being done about it that’s more revolutionary than just paying someone to clean the turbine every once in a while.

Allen Hall: And the contaminants that get into the greases are a huge problem, particularly where there’s any sort of sand, dust that climbs in.

So keeping those joints clear and those rolling surfaces clear is a major effort. And knowing when to relubricate. And, and Matthew, you guys see pitch bearings and all kinds of problems up on blades that are lubricated that have run out of their lifetime early. It does seem like the first thing you see on particularly pitch bearings [00:24:00] is grease on the side of the turbine from them.

Matthew Stead: Yeah. I think that’s– uh, there’s even a special code that the, the visual drone inspection companies have. They’ve got codes for, um, grease and so, yeah, exactly, that’s an early flag. But also dust. You know, sometimes dust from the inserts and from the bolts. Yeah. So it’s, yeah, interesting topic.

Allen Hall: Well, I, I think it’s one of the key pieces to keeping the turbines running.

And I know if you travel a lot around wind turbines, the, the grease is the thing that the technicians always talk about, and there’s so many different tools to go out and look at these things. But lubrication, we gotta get to it. And, and Shell, and SKF, and a number of others are, are working at it to make, hopefully, our lives a little bit easier.

So if you wanna go check out this article by Shell, go visit peswind.com and download a copy today. That wraps up another episode of the Uptime Wind Energy podcast. If today’s discussion sparked any questions or ideas, we’d love to hear from you. Reach out to us on [00:25:00] LinkedIn, and don’t forget to subscribe so you never miss an episode.

So for Yolanda, and Matthew, and an absent Rosie, I’m Allen Hall, and we’ll see you here next week on the Uptime Wind Energy podcast.

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As all literate Americans understand, a) illegal aliens are not eligible for welfare, and b) the vast majority of U.S. welfare dollars go to poor, uneducated white southerners who almost exclusively voted for Trump.

If it weren’t for those two facts, the author of the meme at left would have a good point.

College professors and movie producers in California, and stock analysts in New York aren’t on government assistance.

Trump and Illegal Aliens

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This Is Funny, but it Cuts to the Bone

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Fans of our top comedians will love this video, a fictional reenactment of George Washington’s addressing an open meeting of citizens in the newly formed United States, concerning potential frailties of the soon-to-be-ratified Constitution.

https://www.2greenenergy.com/2026/07/06/george-washington/

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